P ayoffs from information technology (IT) continue to generate interest and debate both among academicians and practitioners. The extant literature cites inadequate sample size, lack of process orientation, and analysis methods among the reasons some studies have shown mixed results in establishing a relationship between IT investment and firm performance. In this paper we examine the structura...

We consider the problem of binary classification when the covariates conditioned on the each of the response values follow multivariate Gaussian distributions. We focus on the setting where the covariance matrices for the two conditional distributions are the same. The corresponding generative model classifier, derived via the Bayes rule, also called Linear Discriminant Analysis, has been shown...

This paper presents the classification of students studying at Forman Christian College University Lahore using some commonly applied classification techniques namely classification trees (CT) using Gini function as splitting rule, linear discriminant analysis (LDA) and multinomial logistic regression (MLR). As per rule set by the university, each student is getting a final grade or label (take...

We introduce functional logistic discriminant analysis (FLDA) which is an extension of the classical method of logistic discriminant analysis to data where predictor variables are functions or curves. FLDA approach can effectively classify functions into two distinct classes by imposing smoothness constraint on the predictor functions and coefficient function by radial basis function expansion ...

conclusions the levels of hemoglobin can be used to predict preeclampsia and monitoring the pregnant women and its regular measure in 3 trimesters help us to identify women at risk for preeclampsia. background preeclampsia is one of the most serious complications during pregnancy with important effects on health of mother and fetus that causes maternal and fetal morbidity and mortality. this st...

In two-group discriminant analysis, the Neyman-Pearson Lemma establishes that the ROC curve for an arbitrary linear function is everywhere below the ROC curve for the true likelihood ratio. The weighted area between these two curves can be used as a risk function for finding good discriminant functions. The weight function corresponds to the objective of the analysis, for example to minimize th...

Traditionally, statistical techniques such as multivariate discriminant analysis and logistic regression analysis have been applied for predicting financial distresses by analyzing financial ratios. In addition to statistical methods, recent studies suggest that backpropagation neural networks (BPNs) and support vector machines (SVMs) can be alternative approaches for classification tasks. Henc...

Classification arises in a wide range of applications. A variety of statistical tools have been developed for learning classification rules from data. Understanding of their relative merits and comparisons help users to choose a proper method in practice. This paper focuses on theoretical comparison of model-based classification methods in statistics with algorithmic methods in machine learning...